课程信息

    第九期2017年北京大学可视化发展前沿研究生暑期学校将于2017年7月8 - 14 日举行。报到日期:7月8日下午。上课时间:7月9-14日上午9:00-12:00,下午14:00-17:00。北京大学可视化发展前沿研究生暑期学校由教育部机器感知与智能重点实验室承办。暑期学校面向全国招生,国内各大院校和研究院所中相关专业的在校硕士、博士研究生和青年教师均可申请,同时可接受少量对可视化领域有浓厚兴趣的优秀高年级本科生。学员参加本项目课程的学习并通过相关的考核将获得由北京大学印制的研究生暑期学校结业证书,并按规定根据学员课程学习完成情况可计4学分研究生成绩。本次暑期学校的重点是大数据的可视化与可视分析。邀请海内外在可视化研究领域具有深厚造诣的知名学者,面向研究生和青年教师系统探讨本领域的前沿理论和研究方法。
    承办单位:教育部机器感知与智能重点实验室
    合作单位:北京图象图形学学会

招生信息

招生对象

暑期学校招收正式学员60-90人,重点招收可视化相关方向硕士研究生、博士研究生。 同时可接受少量青年教师和非高校学员。

招生方式

学员选拔采取自由报名,择优录取的方式。研究生报名需要有导师的推荐信。报名材料由专家委员会审查后决定录取名单。

授课方式

暑期学校授课由教师授课和学生讨论的形式相结合,强调学员的参与,提供多种互动和参与的机会。授课语言为英文。暑期学校毕业要求完成一定的可视化设计工作。

学分学费

经过考核合格,学员获得暑期学校结业证书。国内在校注册学生参加暑期学校学费1400元。暑期学校根据报名情况提供部分非学生身份学费4500元;学费在发出录取通知后支付,开学领取发票。开具发票后不能退款。

提交申请材料

请在提交申请材料页面(注册已截止)填写所需信息后,请推荐人将推荐信电子邮件提交到到邮箱pkuvis[at]pku.edu.cn,即完成全部申请。经过专家委员会审核通过的申请将会收到通知。

课程日程

日期 时间 内容
7 月 08 日 14:00-17:00 Student Registration
7 月 09 日 09:00-10:00 Icebreaker and Introduction
Xiaoru Yuan, Peking University
10:00-12:00 Data Visualization: A Multi-disciplinary Perspective
Qu Huamin, Hong Kong University of Science and Technology
14:00-15:30 Data Visualization: A Multi-disciplinary Perspective
Qu Huamin, Hong Kong University of Science and Technology
15:30-17:00 Visual Analytics via Real-time Interactive 2D Embedding
Jaegul Choo, Korea University
7 月 10 日 09:00-12:00 Visual Analytics Via Real-time Interactive 2D Embedding
Jaegul Choo, Korea University
14:00-17:00 Graphs in Scientific Visualization
Chaoli Wang, University of Notre Dame
7 月 11 日 09:00-10:30 Graphs in Scientific Visualization
Chaoli Wang, University of Notre Dame
10:30-12:00 Immersive Visualization
Lu Aidong, University of North Carolina
14:00-17:00 Security Visualization
Lu Aidong, University of North Carolina
7 月 12 日 09:00-12:00 Interactive Visual Anomaly Detection and its Applications
Nan Cao, TongJi University
14:00-17:00 Lab Visit
7 月 13 日 09:00-12:00 Visual Analytics in Cross Domain Applications
Zhang Jiawan, Tianjing University
14:00-17:00 Interactive Model Analysis
Liu Shixia, Tsinghua University
7 月 14 日 09:00-12:00 Artistic Data Visualization in the Making
Xu Ruige, Syracuse University
14:00-17:00 Project Report

课程内容

    Xiaoru Yuan
    Title: Icebreaker and Introduction
    Time: July 09, 09:00 – 10:00

    Prof. Qu Huamin
    Title: Data Visualization: A Multi-disciplinary Perspective
    Time: July 09, 10:00 - 12:00 & July 09, 14:00 - 15:30
    Abstract: I will first introduce the history of data visualization from a multi-disciplinary perspective and talk about the opportunities and challenges for data visualization in the big data era. I will then present ongoing research projects at HKUST and demonstrate how data visualization helps reveal rumor propagation on social media such as Twitter and WeChat, learning behaviors of students on MOOC platform, and human mobility patterns based on mobile phone and transportation data.
    Bio: Huamin Qu is a full professor in the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology. His main research interests are in visualization and human-computer interaction, with focuses on urban informatics, social network analysis, e-learning, and text visualization. He has co-authored about 100 refereed papers including about 40 papers in the IEEE Transactions on Visualization and Computer Graphics (TVCG). His research has been recognized by many awards including 7 best paper/honorable mention awards, 2009 IBM Faculty Award, 2014 Higher Education Scientific and Technological Progress Award (Second Class) from the Ministry of Education of China, and 2016 Distinguished Collaborator Award from Huawei Nah's Ark Lab. He obtained a BS in Mathematics from Xi'an Jiaotong University, China, an MS and a PhD (2004) in Computer Science from the Stony Brook University.

    Prof. Jaegul Choo
    Title: Visual Analytics via Real-time Interactive 2D Embedding
    Time: July 09, 15:30 – 17:00 & July 10, 09:00 - 12:00
    Abstract: The lecture covers basic and advanced techniques of real-time interactive 2D embedding of high-dimensional data. I will describe various 2D embedding methods ranging from multi-dimensional scaling to t-distributed stochastic neighbor embedding, and its visual analytic applications for real-time interactive 2D embedding.
    Bio: Jaegul Choo (https://sites.google.com/site/jaegulchoo/ ) is an assistant professor in the Dept. of Computer Science and Engineering at Korea University. He has been a research scientist at Georgia Tech from 2011 to 2015, where he also received M.S in 2009 and Ph.D in 2013. His research focuses on visual analytics for high-dimensional data, which leverages both data mining and interactive visualization. He has been publishing in premier venues in both fields such as TVCG, VAST, CG&A, KDD, WWW, WSDM, AAAI, IJCAI, ICDM, TKDD, DMKD, ICWSM, and SDM, . He earned earned the Best Student Paper Award at ICDM in 2016, the Outstanding Research Scientist Award at Georgia Tech in 2015 and the Best Poster Award at IEEE VAST (as part of IEEE VIS) in 2014, and he was nominated as one of the four finalists in IEEE Visualization Pioneers Group dissertation award in 2013.

    Prof. Chaoli Wang
    Title: Graphs in Scientific Visualization
    Time: July 10, 14:00 - 17:00 & July 11, 09:00 - 10:30
    Abstract: An overview of the use of various graph representations and techniques for scientific visualization with case studies of several graph-based visual interfaces (iTree, TransGraph, FlowGraph) from my research.
    Bio: Dr. Chaoli Wang is an associate professor of computer science and engineering at University of Notre Dame. He received a Ph.D. degree in computer and information science from The Ohio State University in 2006. Dr. Wang's main research interest is data visualization, in particular on the topics of time-varying multivariate data visualization, flow visualization, high-performance visualization, and information-theoretic algorithms and graph-based techniques for big data analytics. He is a recipient of the NSF CAREER Award (2014), two best paper awards at IS&T/SPIE VDA (2013, 2015), and an honorable mention at IEEE PacificVis (2013).

    Prof. Aidong Lu
    Title: Immersive Analytics
    Time: July 11, 10:30 - 12:00
    Abstract: This course will introduce an emerging topic of visualization – immersive analytics, which investigates how new interaction and display technologies can be used to support analytical reasoning and decision making. The aim is to provide multi-sensory interfaces for analytics software that support collaboration and allow users to immerse themselves in their data and designs. The course will provide the vision of immersive analytics using the latest virtual and augmented devices, as well as the latest work of design and evaluation of immersive analytics from venues including IEEE VisWeek and IEEE VR.
    Title: Security Visualization
    Time: July 11, 14:00-17:00
    Abstract: Due to various malicious frauds and attacks, security has become an important issue to many real-life applications, especially for network systems. This course will provide an overview of security visualization. We will introduce important techniques of how visualization can be used to assist interactive analysis and anomaly detection. Techniques of network security visualization will be elaborated to demonstrate the usage of security visualization. The latest work will be selected from venues including IEEE VisWeek, Eurovis, and IEEE Symposium of Visualization on Security.
    Bio: Aidong Lu is an Associate Professor in the Department of Computer Science at the University of North Carolina at Charlotte. Her main research areas are Visualization and Visual Analytics, and her recent interests are on security visualization and immersive analytics. Her research has been recognized by the IEEE Visualization Best Paper Award 2002, Department of Energy Early Career Award 2006, and IEEE Visual Analytics Best Poster Award 2012. She obtained the degrees of BS and MS from Tsinghua University in 1999 and 2001 respectively and PhD from Purdue University in 2005.

    Prof. Nan Cao
    Title: Interactive Visual Anomaly Detection and its Applications
    Time: July 12, 09:00 - 12:00
    Abstract: In this lecture, we will introduce visualization techniques that are designed and developed for supporting anomaly detection in various application domains such as social media, urban computing, and health informatics. The visualization design principles and guidelines that are specifically proposed for anomaly detection will also be introduced via concrete examples during the lecture.
    Bio: Nan Cao is a national 1000 young talent program expert. He is currently a professor at Tongji University in China. He is also the founder and the director of the Intelligent Big Data Visualization Lab. Before joining Tongji University, Nan Cao was a research staff member at IBM T.J. Watson Research Center and he worked for IBM for 10 years during 2005 - 2015. His primary expertise and research interests are information visualization and visual analysis. He is specialized in producing novel visualization techniques to represent and analysis complex (big, heterogeneous, multidimensional, dynamic) real world data. He has over 50 publications and filed approximately 40 patents in the fields of Information Visualization, Visual Analytics, Data Mining, and Human Computer Interaction. He served as a guest editor of IEEE Transactions on Multimedia, ACM Transactions on Interactive Intelligent Systems, and ACM Transaction on Intelligent Systems and Technology. He is the program committee member of many important international conferences such as IEEE VAST, EuroVis, PacificVis, AAAI, SDM, and IJCAI.

    Prof. Jiawan Zhang
    Title: 面向多学科交叉的可视分析技术与应用
    Time: July 13, 9:00-12:00
    Abstract: 通过将机器智能与人工智能有机结合,可视分析可有效帮助人类解决现实世界的分析难题,是当今混合智能和大数据分析的关键技术。课程将重点探讨多学科交叉背景下的可视分析研究与应用话题,涉及可视分析在文物保护、智慧城市等领域的应用,以及在沉浸式可视化分析方向的初步探索。

    Prof. Shixia Liu
    Title: Interactive Model Analysis
    Time: July 13, 14:00-17:00
    Abstract: In most AI applications, machine learning models are often treated as a black box. Because of lacking of a comprehensive understanding of the working mechanism of these models, it is hard to build an effective two-communication between a human and a computer, which limits the further adoption of the models. To solve this problem, we have developed a set of visual analytics approaches to help users understand, diagnose, and refine a machine learning model. This talk presents the major challenges of interactive machine learning and exemplifies the solutions with several visual analytics techniques and examples. In particular, we mainly focus on introducing the following three aspects: 1) create a suite of machine learning techniques that produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); 2) develop a set of visual analytics techniques that enable human users to understand and diagnose machine learning models; 3) a semi-supervised model refinement mechanism. Based on these, we develop an interactive model analysis framework, which is exemplified by deep learning, ensemble learning, and the topic model.

    Prof. Xu Ruige
    Title: Artistic Data Visualization in the Making
    Time: July 14, 09:00-12:00
    Abstract: In recent years we have seen an increasing of interest in data visualization in the artistic community. Many data-oriented artworks use sophisticated visualization techniques to express a point of view or persuasive goal. Meanwhile the attitude that visualizations can be used to persuade as well as analyze has been embraced by more people in the information visualization community. This talk shares my experience and reflection in creating data visualization as artwork via case study of recent projects. It presents a workflow from conceptual development, data analysis, to algorithm development, procedural modeling, and then final image production. It hopes to offer insight into the artist’s effort of finding balance between persuasive goals and analytic tasks. Furthermore, it raises the question of the roles of artistic data visualization played in assisting people to comprehend data and the influence of this artistic exploration in visualization might have injected in shifting public opinions.
    Bio: Rebecca Ruige Xu currently teaches computer art and animation as an Associate Professor in College of Visual and Performing Arts at Syracuse University. Her artwork and research interests include artistic data visualization, visual music, experimental animation, interactive installations, digital performance and virtual reality. Her recent work has been appeared at: IEEE VIS Arts Program; SIGGRAPH & SIGGRAPH Asia Art Gallery; ISEA; Ars Electronica; Museum of Contemporary Art, Italy; Los Angeles Center for Digital Art, USA; Expressive 2015, CAe+SBIM+NPAR, Istanbul, Turkey; FILE– Electronic Language International Festival, Brazil; Techfest -Technical Arts Exhibition, India; Colloquium culture and digitization, Switzerland; CYNETart, Germany; International Digital Art Exhibition, China; Boston Cyberarts Festival, USA. Xu served as a panelist on the Media Arts Advisory Panel for the U.S. National Endowment for the Arts. She also worked for New York State Council on the Arts and Missouri Arts Council. She has been a research fellow at Transactional Records Access Clearinghouse, Syracuse University since 2011, and is currently on the Executive Committee for the Association of Chinese Artists in American Academia.

特邀讲师

袁晓如

北京大学

屈华民

香港科技大学

Chaoli Wang

University of Notre Dame

Aidong Lu

University of North Carolina

刘世霞

清华大学

曹楠

同济大学

张加万

天津大学

Jaegul Choo

Korea University

Ruige Xu

Syracuse University

历届讲者

Peter Eades

University of Sydney

Torsten Möller

University of Vienna

Jinwook Seo

Seoul National University

Kai Xu

Middlesex University

Xiaolong Zhang

Pennsylvania State Univ.

Seokhee Hong

University of Sydney

Lei Shi

中科院软件所

Hanqi Guo

Argonne National Lab.

Natalia Andrienko

IAIS

Gennady Andrienko

IAIS

Huamin Qu

HKUST

HanWei Shen

The Ohio State Univ.

Yifan Hu

AT&T Labs

Wei Chen

Zhejiang Unviersity

Claudio T. Silva

CUSP, USA

Ye Zhao

Kent State University

Baoquan Chen

Shandong University

Klaus Mueller

Stony Brook Unviersity

Daniel Keim

Universität Konstanz

Maurizio Patrignani

Roma Tre University

Yingqing Xu

Tsinghua University

Gary Meyer

University of Minnesota

Alfred Inselberg

Tel Aviv University

Hans Hinterberger

ETH Zürich

Jing Yang

Univ. of North Carolina

Michael McGuffin

ÉTS

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